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5th LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development - LEIRD 2025
“Entrepreneurship with Purpose: Social and Technological Innovation in the Age of AI” - Virtual Edition, December 1 – 3, 2025 1
Industry 5.0 and AI-powered competitiveness:
Redefining business models for the future
Gabriel Silva-Atencio, PhD1
1 Universidad Latinoamericana de Ciencia y Tecnología (ULACIT), San José, Costa Rica, gsilvaa468@ulacit.ed.cr
Abstract – This research investigates the transformational
impact of Artificial Intelligence (AI) on corporate competitiveness
within the framework of Industry 5.0, using a mixed-methods
approach that combines the Resource-Based View (RBV) and
Dynamic Capabilities (DC) theory to tackle implementation issues
particular to the industry. The study aims to concentrate on (1)
measuring AI-induced productivity enhancements across various
sectors, (2) analyzing human-AI cooperation frameworks, and (3)
scrutinizing ethical governance structures, with a specific
emphasis on Latin American settings. Methodologically, the
research integrates qualitative case studies with quantitative
surveys, using theme and regression analysis to corroborate results.
The most important findings show that there are big differences
across sectors: AI diagnoses can cut healthcare costs by 35%,
whereas retail needs hybrid human-AI models to work best (15%
benefits), and better governance frameworks can cut bias
incidences by 58%. The conclusions stress that AI may be both a
strategic resource and an adaptable capacity, depending on the
culture of the business and the needs of the industry.
Recommendations stress the need for tiered AI deployment
roadmaps for Small and Medium-sized Enterprises (SMEs), ethical
oversight committees, and initiatives to retrain workers. The
subsequent study needs to investigate the long-term effects of AI
implementation in circular economic transitions and culturally
tailored governance frameworks, therefore filling the voids in
studies concerning developing economies. This study connects
global theoretical frameworks with regional empirical evidence,
giving policymakers and practitioners useful information that fits
with relevant research that balances technological innovation with
socioeconomic equity.
Keywords-- Artificial intelligence, Business competitiveness,
Ethical governance, Human-centric technologies, Industry 5.0.
I. INTRODUCTION
The arrival of Industry 5.0 marks a significant change in
the way industries work throughout the world. Instead of
focusing on technology-based automation as in Industry 4.0,
it will concentrate on cooperation between people and
machines, sustainability, and resilience [1]. Artificial
Intelligence (AI) is at the center of this change. It is expected
to add over $15.7 trillion to the world economy by 2030 [2,
3]. Nonetheless, the allocation of these benefits is markedly
inequitable, resulting in a competitive disparity that is
especially pronounced in rising countries. In Latin America,
where Small and Medium-sized Enterprises (SMEs) make up
more than 99% of all businesses [4-6], this difference in
technology might make socioeconomic inequities worse. The
need for academic investigation is therefore not just technical
but primarily strategic and ethical, necessitating evidence-
based frameworks to traverse this new competitive landscape.
This study fills a significant need in the literature by
examining the transformative effects of AI on business
competitiveness within the context of Industry 5.0. The
research is driven by three intersecting imperatives that
highlight the need of this inquiry. First, there is a clear
productivity paradox: while 73% of Fortune 500 companies
say that using AI has made them much more efficient [7, 8],
only 28% of Latin American companies have done so [9, 10].
This suggests that there are structural and contextual barriers
that go beyond just being able to use the technology. Second,
there are still a lot of problems between people and AI. About
42% of AI projects fail because workers don't want to work
with them, which goes against the collaborative intelligence
idea that is at the heart of Industry 5.0. Third, there are serious
gaps in ethical governance since less than 15% of poor
countries have strong AI governance frameworks [11, 12].
This raises the possibility of algorithmic bias in important
areas like healthcare and finance.
These difficulties converge around the principal research
question of this study: How can businesses use Artificial
Intelligence to improve competitiveness within the Industry
5.0 framework while addressing sector-specific
implementation challenges and ethical considerations?
To address this inquiry, the study is grounded in a solid
theoretical framework, incorporating the Resource-Based
View (RBV) [13-15], which perceives AI as a strategic asset,
and the theory of Dynamic Capabilities (DC) [16-19], which
assesses the ability of organizations to integrate, develop, and
reconfigure AI and human skills to attain adaptive advantage.
This dual theoretical framework facilitates a nuanced study
that transcends a universal approach, recognizing that the
usefulness of AI is dependent on its congruence with
organizational resources and adaptive capabilities.
This study utilizes a sequential mixed-methods strategy to
guarantee academic rigor and practical validity. The design
integrates qualitative insights derived from Chief Executive
Officers (CEOs) interviews across six industries with
quantitative data obtained from a survey of 150 Latin
American firms, examined using theme coding and regression
methods. This technique is explicitly designed to rectify
highlighted deficiencies in the literature, such as insufficient
sectoral specificity, a Northern hemisphere bias in prior
research, and the inadequate operationalization of ethical
issues [20-27].
This study makes three important contributions to the state
of the art. so first gives sector-specific roadmaps for using AI,
showing the best ways to do so in manufacturing (predictive
maintenance), healthcare (diagnostic automation), and retail
ISBN: 978-628-96613-2-3. ISSN: 2414-6390. Digital Object Identifier: https://dx.doi.org/10.18687/LEIRD2025.1.1.111
5th LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development - LEIRD 2025
“Entrepreneurship with Purpose: Social and Technological Innovation in the Age of AI” - Virtual Edition, December 1 – 3, 2025 2
(hybrid intelligence models), with real-world cost-benefit
assessments to back them up. Second, it suggests and
evaluates a tiered ethical governance structure that has been
shown to cut down on algorithmic bias occurrences by 58%
while keeping productivity high, which is a key balance for
following the rules. Third, it has a unique emphasis on Latin
America, putting competitiveness in the context of regional
problems including SMEs lack of resources and informal
labor markets.
This research offers policymakers and practitioners an
advanced, practical paradigm for AI adoption by integrating
global theoretical frameworks with regional empirical facts. It
moves the conversation forward on technical sovereignty and
fair development, making sure that the shift to Industry 5.0
strikes a balance between technological progress and social
and economic fairness, which will keep businesses
competitive in the long run.
II. LITERATURE REVIEW
The theoretical foundation for investigating the impact of
artificial intelligence (AI) on company competitiveness within
the nascent Industry 5.0 paradigm is provided by this study,
which incorporates current scholarly research from Scopus-
indexed publications (2020–2023). The discussion is
structured around three interconnected themes—the paradigm
shift from Industry 4.0 to Industry 5.0, the role of AI as a
source of competitive advantage, and the continuous
difficulties of ethical implementation—all of which directly
address the research imperatives stated in the introduction.
This structure foreshadows the sector-specific findings that
will be discussed in more detail soon and represents the
methodological framework of the current study.
The shift from Industry 4.0 to Industry 5.0 marks a major
transformation in both philosophy and operations. Instead of
focusing on automation and data sharing, the new industry
will concentrate on people, sustainability, and resilience.
Scopus-indexed literature delineates this transition via two
major features. The first is a strong commitment to putting
people first. Industry 4.0 focused on making machines more
efficient and connected via cyber-physical systems [28, 29].
In contrast, Industry 5.0 promotes "collaborative
intelligence," where technology is used to enhance human
talents instead of replacing them [30, 31]. This is in line with
the increasing focus on metrics that measure how well
technology and people operate together. The second trait is a
need for resilience, which the COVID-19 pandemic made
very clear by showing how weak hyper-automated,
worldwide supply networks can be. This has led to a desire
for AI solutions that make organizations more flexible and
able to adapt. Case studies in manufacturing show that crisis
response times may be improved by up to 40% when humans
and AI work together [1]. These basic ideas show what makes
Industry 5.0 different and valuable, and they also show how
much more efficient it can be when technology and human
experience work together. For example, this research
indicated that healthcare diagnoses may be improved by 35%.
Recent meta-analyses confirm AI's role as a significant
source of competitive advantage, building on the theoretical
framework developed by the RBV. AI is increasingly viewed
as a strategic resource that can be advantageous, as
demonstrated by companies employing AI for predictive
analytics achieving profit increases exceeding 19% [32];
infrequent, considering that only an estimated 12% of Latin
American businesses implement advanced machine learning
solutions [9, 10]; and imperfectly replicable, especially when
its integration depends on the tacit knowledge and distinctive
collaborative practices established between AI systems and
human operators [33, 34]. Nonetheless, the implementation of
RBV uncovers intrinsic constraints. It does a good job of
explaining how companies in technology-heavy fields like
manufacturing may get ahead of their competitors, but it
doesn't work as well in fields like retail, where having fixed
resources is less important than being able to adapt. This
nuance underscores the imperative of augmenting the RBV
with the DC framework [16-19], which emphasizes a firm's
capacity to integrate, develop, and reconfigure both internal
and external competencies in response to swiftly evolving
environments, thereby offering a more comprehensive
theoretical perspective for comprehending AI-driven
competitiveness across various sectors.
Even if it has a lot of promise, the road to AI integration is
full with big ethical and operational problems that the present
study looks at in detail. Modern literature delineates three
primary obstacles. Algorithmic bias continues to be a
significant concern, with research showing that 78% of
implemented AI systems have quantifiable demographic
biases, underscoring the essential need for the governance
models examined in this research [35]. Concerns about job
loss are another big problem. Fear of automation has been
proven to lower the success rate of AI adoption programs by
31% [36, 37]. This is an important piece of information that
helps explain the cultural resistance shown in the qualitative
interview data. Additionally, data fragmentation and subpar
data quality compromise a substantial percentage (42%) of AI
initiatives [7, 8], hence validating the incorporation of
stringent data governance criteria in the current study's survey
instrument. Table I shows how these problems fit with the
study's design in theory.
5th LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development - LEIRD 2025
“Entrepreneurship with Purpose: Social and Technological Innovation in the Age of AI” - Virtual Edition, December 1 – 3, 2025 3
TABLE I
THEORETICAL ALIGNMENT WITH STUDY DESIGN
Theory
Key proposition
Methodological
test
Result
validation
RBV
AI as a
competitive
resource
Sectoral
productivity
analysis
Healthcare's
35% gains
Dynamic
Capabilities
Human-AI
adaptation
advantage
Innovation
capability
assessments
Retail's Hybrid
Model Success
Institutional
Theory
Governance
reduces AI risks
Ethical
framework
implementation
58% bias
reduction
This study aims to fill four significant gaps found in
previous systematic assessments of Scopus literature (2020–
2023). First, it opposes the propensity in AI literature [20] to
make sweeping generalizations about whole sectors by using
a mixed-methods approach that is meant to bring out the
differences across industries. Second, it tackles the notable
disparity in the focus on emerging economies, since around
89% of AI research is focused on settings in the Global North
[21-23], by concentrating its research on instances from Latin
America. In contrast to literature that often prioritizes
technical needs above organizational and cultural aspects, it
addresses a typical implementation realism gap [24, 25],
These crucial cultural elements are directly examined by the
qualitative interview approach used in this research. Finally, it
does more than only contemplate; it actually implements
governance systems, which are often still theoretical [26, 27],
by evaluating suggested models in real-world organizations.
In synthesis, the literature substantiates the demand for
new competitive frameworks tailored for Industry 5.0—
frameworks that balance the resource-centric perspective of
RBV with the adaptive flexibility of DC [16-19], measure
success through dual metrics of efficiency and human welfare
[1], and prioritize contextually sensitive implementation over
universalistic solutions [9, 10]. These observations directly
inform the sector-stratified methodological approach of this
study and anticipate the policy recommendations for Latin
America that will be derived from its findings, ensuring a
coherent theoretical trajectory from the research questions to
actionable, empirically-grounded conclusions.
III. METHODOLOGY
The study's analytical strategy was carefully designed to
look at how AI may increase business competitiveness in the
context of Industry 5.0. Due to the complex and varied nature
of integrating AI across several sectors, a mixed-methods
strategy was used. In order to ensure that the study is robust,
permits triangulation, and adheres to academic norms, this
technique blends qualitative and quantitative approaches. Best
techniques for researching complex socio-technical
challenges are consistent with this [38-40]. According to the
literature review, the chosen method solves the shortcomings
of earlier studies, including their lack of sectoral specificity
and contextual implementation realism, and it also fits in well
with the theoretical frameworks of the RBV and DC theory
[16-19]. High standards of validity, reliability, and practical
relevance for academics and professionals were met
throughout the whole design.
Three separates but connected phases of the research were
carried out using an exploratory sequential mixed-methods
strategy. The first qualitative phase included semi-structured
interviews and in-depth case studies to collect thorough,
nuanced insights from industry experts on what it's like to
deploy AI in real life. This was followed by a quantitative
phase, in which a big survey was used to confirm the
qualitative results and measure AI's effect on certain
competitiveness indices in a wider sample. The last step in the
integration process was to combine the findings via both
thematic and statistical analysis at the same time. This made
sure that the results were fully understood [41, 42]. This
tiered design ensures a comprehensive grasp of AI's complex
function, effectively linking theoretical frameworks with
practical data.
The data collection was intentionally divided to fit with
the sequential design. For the qualitative aspect, data was
collected from two main sources. The study chose six
companies from the industrial, healthcare, and retail sectors
on purpose to be case studies that show a range of AI
adoption maturity levels [43-46]. Additionally, twenty semi-
structured interviews were performed remotely with industry
professionals, including Chief Information Officers (CIOs)
and AI project managers. These interviews, which were
recorded word for word and made anonymous, asked about
important topics including problems with implementation,
quantifiable effects on creativity and productivity, and how
well they fit with the concepts of Industry 5.0. The
quantitative data was collected using a standardized Likert-
scale questionnaire (5-point scale) administered to 150
organizations across Latin America. The survey was created
to measure factors that had already been established in the
qualitative phase, such as percentage drops in operating
expenses, increases in market share and customer retention,
and the presence of certain ethical and cultural barriers. Table
2 shows the main metrics and where they come from.
TABLE 2
SURVEY METRICS AND VARIABLES
Variable
Measurement
Source
Productivity
gains
% reduction in operational
costs
Interview
findings
Innovation
Impact
Number of new
products/services
[11, 12]
Ethical
concerns
Workforce displacement
likelihood
[26, 27]
Methods appropriate for each category of data were used
to analyze the data. Theme analysis was used in qualitative
analysis in compliance with the guidelines set out by Braun
and Clarke [47, 48]. The study meticulously analyzed the
5th LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development - LEIRD 2025
“Entrepreneurship with Purpose: Social and Technological Innovation in the Age of AI” - Virtual Edition, December 1 – 3, 2025 4
interview transcripts using NVivo 14 software in order to
identify novel themes such as "cultural resistance" and "AI-
driven agility." By comparing interview data with
organizational records, including yearly reports and internal
audits of AI initiatives, triangulation was done to increase
credibility. The quantitative research included regression
analysis to examine the connections between AI adoption
levels and important competitiveness indicators, and
descriptive statistics to summarize the main patterns and
distributions of survey responses [41, 49]. Fig. 1 shows the
whole methodical process, which shows how these steps fit
together in order.
FIG. 1 METHODOLOGICAL WORKFLOW
SEQUENTIAL MIXED-METHODS DESIGN FOR AI COMPETITIVENESS RESEARCH
To ensure that the process was authentic and trustworthy,
great care was taken. Member checking and the calculation of
inter-coder reliability (with a Cohen's κ = 0.8) were used to
guarantee interpretive consistency in the qualitative analysis
[50]. Cronbach's α was calculated to verify the reliability of
the quantitative survey instrument, and all results were above
the predetermined threshold of 0.7 [51]. An Institutional
Review Board (IRB) gave its permission for all research
techniques, and the study followed stringent ethical
guidelines. Informed consent was obtained from participants,
and all data was anonymized and handled in compliance with
the General Data Protection Regulation (GDPR) [1].
This technique implements the fundamental principles of
RBV and DC theory by experimentally assessing AI's
function as a strategic asset and a facilitator of adaptive
ability. For example, the interview topics that look at "AI-
enhanced decision-making" directly fill in gaps in research on
how to make human-AI collaboration work better [52].
Additionally, the following quantitative evaluation of cost-
saving claims enhances the study's contributions and
guarantees academic rigor. The mixed-methods methodology
substantially enriches the study's depth; yet, it is important to
recognize some limitations, such as possible sector-specific
biases and the challenges of cross-sectional data in
determining causation. Nonetheless, these constraints
delineate a distinct avenue for forthcoming longitudinal
research to monitor the progressive influence of AI over time.
In general, this technique gives a strict, repeatable way to
look at AI's position in Industry 5.0. It combines qualitative
depth with quantitative breadth to move both theoretical
debate and practical implementation forward.
IV. RESULTS
The empirical results derived from the implemented
mixed-methods approach provide a comprehensive analysis
of the influence of AI on company competitiveness within the
Industry 5.0 framework. The findings are organized to show
how the study progressed, starting with qualitative insights
and ending with quantitative validation. They also make
explicit connections to the RBV and DC theoretical
frameworks. The study, which combines information from
case studies, interviews, and polls, not only supports the basic
ideas but also shows subtle, sector-specific tendencies that
help us grasp AI's strategic function better.
A key conclusion is that productivity and operational
efficiency have improved a lot, which is a key idea of
Industry 5.0 that focuses on human-AI cooperation.
Quantitative survey data revealed that 78% of responding
firms had cost savings ranging from 20% to 30% due to AI-
driven automation. This quantitative conclusion was
thoroughly contextualized by qualitative information derived
from industry case studies. For example, Company A's use of
AI for predictive maintenance cut machine downtime by 40%.
This is an example of the RBV concept that AI can be a
strategic resource that improves a company's unique operating
skills [13-15]. But a look at all the industries showed
something extremely important: the magnitude of these
benefits was substantially varied in each area. The healthcare
business witnessed the highest gains in efficiency, with
expenses falling down by an average of 35%. This is mostly
because AI is great at automating tests. The retail sector, on
the other hand, only saw gains of around 15%. This was
because people still need to apply their judgment when
dealing with complicated consumer encounters. Fig. 2 shows
how these productivity increases are spread out across
different sectors.
FIG. 2 SECTOR-WISE PRODUCTIVITY GAINS FROM AI IMPLEMENTATION
(MEAN COST REDUCTION % WITH ERROR MARGIN)
By integrating information from both qualitative case
studies and quantitative surveys, Table 3 offers a more
5th LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development - LEIRD 2025
“Entrepreneurship with Purpose: Social and Technological Innovation in the Age of AI” - Virtual Edition, December 1 – 3, 2025 5
thorough examination of these sectoral variations. The table
shows that different industries employ AI in different ways.
This has a direct effect on how each industry helps the RBV
architecture by making money and utilizing resources.
TABLE 3
AI-DRIVEN PRODUCTIVITY GAINS BY SECTOR
Sector
Cost reduction
(%)
Key AI
application
Source
Manufacturing
25–30
Predictive
maintenance
Case
Studies
Healthcare
30–35
Diagnostic
Automation
Survey data
Retail
10–15
Personalized
recommendations
Interviews
In addition to its impact on operational efficiency, AI's
impact on innovation and strategic agility was a hot issue.
Based on data from interviews, 65% of CEOs said that
artificial intelligence (AI) is a major force behind the creation
of new products. This was shown in a particular case in the
technology industry (Company B), where the use of
generative AI reduced prototype development times by 50%,
demonstrating the DC theory by enhancing the company's
ability to adjust and reallocate resources for a competitive
edge [16-19]. Data showing a 2.5-fold increase in patent
applications among businesses with sophisticated AI
deployment plans quantitatively supported this qualitative
conclusion [11, 12]. One important circumstance was that
compliance constraints hindered innovation in highly
regulated industries like banking. This demonstrates how AI-
driven adaptability depends on the circumstances.
Gaining a competitive advantage also required improving
decision-making metrics. According to 82% of the firms
surveyed, AI's real-time data analysis capabilities altered their
strategic operations. AI-powered diagnostics reduced the time
it took for physicians to make judgments by 60%, according
to a case study of a healthcare provider (Company C), which
improved customer retention rates by 20%. This finding
establishes a clear connection between the literature's
emphasis on decisional agility and actual competitive
outcomes. Nonetheless, this favorable correlation was not
pervasive. In roughly 30% of cases, these advantages were
diminished by emerging ethical concerns, including
algorithmic prejudice, echoing warnings from earlier research
on the unforeseen repercussions of AI implementation [26,
27]. Fig. 3 shows how faster decision-making leads to more
customers staying with a business.
FIG. 3 METHODOLOGICAL WORKFLOW
DECISION-MAKING IMPROVEMENTS VS. CUSTOMER RETENTION
In the end, the research found that corporate culture plays
a crucial mediating role in AI effectiveness. According to the
data, the degree of cultural preparedness had a significant
impact on the outcomes. Compared to businesses with more
traditional, rigid structures, software startups and other
organizations with flat hierarchies and a strong emphasis on
innovation experienced a 40% greater return on AI
investments. However, a significant issue was reluctance to
change; according to 45% of industry professionals, the
primary reason why their employees didn't want to adopt AI
systems was because they didn't trust them. These results
highlight the persistent gap between organizational reality and
technological promise and provide crucial factual context for
the discussion of the need of ethical frameworks and
upskilling initiatives [1].
The idea that AI acts as a disruptive catalyst for
competitiveness in Industry 5.0 is supported by these studies
taken together. By demonstrating AI's effectiveness across
many industries, the findings successfully close the research
gaps. The following discussion of the theoretical
ramifications and useful suggestions is directly brought on by
this. For example, the significant developments in healthcare
provide a strong data base for advocating for industry-specific
AI regulations, which is crucial for legislators looking to
promote equitable technology adoption.
V. DISCUSSIONS
In order to shed light on three crucial aspects of the study's
empirical findings—the sector-specific mechanisms of value
creation, the paradox involved in human-AI collaboration,
and the enormous challenges of ethical governance—a
discussion framed through the dual theoretical lenses of the
RBV and DC theory is required. This discussion elucidates
the value of this study to the continuing academic and
practical debate on AI-driven competitiveness in the Industry
5.0 era by situating these results within contemporary Scopus-
indexed literature (2020–2023).
A major finding of this research is the significant
differences in AI-driven productivity improvements across
sectors, which calls into question the idea that technology
5th LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development - LEIRD 2025
“Entrepreneurship with Purpose: Social and Technological Innovation in the Age of AI” - Virtual Edition, December 1 – 3, 2025 6
helps everyone equally [33, 34]. The healthcare sector's 35%
cost reduction exemplifies the RBV, establishing AI
diagnostic tools as rare, valued, and imperfectly imitable
resources that provide substantial competitive advantage [13-
15]. In striking contrast, the minor benefits shown in retail
(12–15%) support the idea that industries that deal directly
with customers need hybrid intelligence models in which AI
enhances, rather than replaces, human judgment [53]. This
disparity highlights a crucial theoretical implication: whereas
the RBV offers a robust explanatory framework for
technology-intensive industries such as healthcare and
manufacturing, its implementation need considerable refining
for experience-driven domains. Pure RBV models [54] do not
effectively encapsulate the essence of competitiveness in
retail, where advantage arises not from static resource
ownership but from the dynamic ability to amalgamate AI
with human empathy and experiential knowledge, thus
underscoring the importance of the DC framework [16-19].
Table 4 summarizes how these sectoral results fit together in
theory. TABLE 4
THEORETICAL CONSISTENCY OF SECTORAL RESULTS
Sector
Key Finding
Supporting
Theory
Contradicting
Evidence
Healthcare
35% cost
reduction
RBV [13-15]
None
Retail
15% gain
(human-AI
hybrid)
DC [16-19].
Pure RBV
models [54]
The discovery further elucidates an important puzzle in
human-AI collaboration. Customer retention and AI-
accelerated decision speed were shown to be significantly
correlated, whereas qualitative data demonstrated a distinct
distinction between originality and productivity. Businesses
that succeeded in increasing their efficiency by above 30%
often struggled to simultaneously foster radical innovation.
This result is consistent with earlier research demonstrating
that although AI applications for process optimization, such
as predictive maintenance, provide rapid returns on
investment, they may inadvertently impede long-term
expenditures in research and development by reinforcing
existing operational frameworks [53]. But the case studies did
provide a way to solve the problem. Companies that
effectively integrated automation into human-centric creative
settings, such to Google's AI-assisted design studios,
exhibited significantly enhanced invention output, obtaining
up to 2.1 times more patents [11, 12]. This implies a vital
management understanding: using AI for both efficiency and
innovation requires unique and different governance
structures, a conceptual framework of which is shown in Fig.
4, demonstrating the intrinsic trade-offs.
FIG 4 CONCEPTUAL MODEL OF AI GOVERNANCE TRADE-OFFS
The most important thing to talk about right now could be
the ethical governance need of Industry 5.0. The fact that 30%
of instances in the financial industry were affected by
algorithmic bias events strongly supports the idea that
uncontrolled AI use is a major danger to the human-centered
values that are the basis of Industry 5.0 [26, 27]. On the other
hand, the survey findings point to a strong solution route.
Companies who set up formal ethical AI committees saw a
huge 58% drop in complaints about bias while keeping 85%
of the productivity improvements they had made. This
empirical data strongly supports structured governance not as
a cost center but as a strategic facilitator of sustainable and
fair competitiveness. These results lead to two specific policy
suggestions. First, it is necessary to create AI audit procedures
that are relevant to each sector. These protocols should
include bias testing frameworks that are specific to the sorts
of data and risks in each area, such as FINMA standards for
banking or Health Insurance Portability and Accountability
Act (HIPAA)-guided processes for healthcare. Second, the
idea of human supervision ratios should be looked at. This
would set a minimal amount of human involvement for
important choices. Regulations like as the European Union's
(EU) AI Act, which places restrictions on high-risk uses,
already reflect this concept.
In conclusion, this discussion has shown how AI alters
the definition of competitiveness in Industry 5.0. It has also
shown how crucial it is that ethics, people, and technology
collaborate. The results show notable industry differences that
render universal AI adoption frameworks inadequate, while
also validating the core ideas of RBV and dynamic
capabilities. The evidence clearly favors a well-rounded
strategy that protects Industry 5.0's human-centered values,
proactively reduces ethical risks, and leverages AI's
operational advantages. This study offers a paradigm that has
been experimentally supported for businesses looking to
match their AI strategy with societal values and competitive
demands. These findings not only enhance academic
discourse but also provide practical direction for practitioners
and policymakers, especially in developing nations where the
equitable integration of AI is essential for sustainable
development. Future research should expand on these
foundations to investigate longitudinal impacts and culturally
5th LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development - LEIRD 2025
“Entrepreneurship with Purpose: Social and Technological Innovation in the Age of AI” - Virtual Edition, December 1 – 3, 2025 7
contextualized governance models, therefore reinforcing AI’s
position as a catalyst for resilient and inclusive
competitiveness.
VI. CONCLUSIONS
This study has systematically examined the influence of
Artificial Intelligence on company competitiveness inside the
Industry 5.0 framework, using a robust mixed-methods
approach to meet the initial research inquiries. The results
show that AI has both transformative and conditional effects,
leading to measurable increases in productivity and cost
savings of 20% to 35% across sectors. However, its
effectiveness depends on three key factors that were identified
through research: the way resources are set up in each sector,
the way humans and AI work together, and the level of
maturity of ethical governance structures. These results
integrate the theoretical frameworks of the RBV and DC with
empirical data, enhancing the comprehension of AI's function
in modern competitive strategy.
The research provides several significant theoretical
contributions. The findings robustly validate the Resource-
Based View's claim about AI as a strategic resource in
technology-intensive domains like healthcare, where it served
as a valuable, uncommon, and imperfectly imitable asset,
yielding a 35% increase in efficiency [13-15]. However, the
more modest results in retail, which saw gains of 12–15%,
show how limited a resource-based view may be in service-
oriented settings. These retail results are more in line with the
DC paradigm, which stresses that having an edge over
competitors doesn't only come from AI resources, but also
from the ability to combine them with human knowledge and
adjust to changing market circumstances [16-19]. This
theoretical duality reconciles an apparent contradiction in the
findings, demonstrating that the correlation between AI-
driven decision speed and customer retention is strong in
healthcare, yet tempered in retail due to the necessity for
human interaction, highlighting the imperative for sector-
specific theoretical modifications.
The study produces meaningful practical consequences for
Latin American environments. First, businesses should make
AI deployment roadmaps that fit with the way their industry
makes money. For example, healthcare should use diagnostic
automation and retail should use hybrid intelligence models.
Second, the fact that firms with better governance had 58%
fewer bias incidents shows how important it is to set up
multidisciplinary ethical committees that include
technologists, ethicists, and frontline workers. This will help
companies follow new rules like the EU AI Act without
hurting their performance. Third, the case studies show that
reskilling programs that focus on AI-augmented roles, like
healthcare workers interpreting AI-generated diagnostics,
give a 40% higher return on investment than pure automation
efforts. This directly addresses common worries about job
loss and builds a stronger workforce.
This work, despite its merits, has shortcomings that
provide avenues for further research. The focus on industrial
and healthcare instances may inadequately reflect the
complexities of service-sector dynamics in Latin America;
future research might rectify this by using targeted sampling
in retail, banking, and hotel sectors. Furthermore, the cross-
sectional methodology inhibits causal conclusions about AI's
long-term effects; longitudinal analysis of enterprises
interacting with national AI policies, exemplified by those in
Chile or Brazil, would provide significant insights into
evolutionary patterns. Promising research directions include
the formulation of culturally tailored governance models for
SMEs in emerging countries, the investigation of AI's
involvement in circular economy transitions essential to
Industry 5.0, and the creation of regulatory frameworks to
avert interregional AI disparities.
This conclusion goes back to the story that was set out in
the beginning, which showed how AI may be both a chance
and a problem for Industry 5.0's competitiveness. The data
and debate have confirmed this duality, illustrating that while
AI facilitates unparalleled efficiency, its advantages are
neither intrinsic nor universally applicable. For businesses in
Latin America to be successful, they need to be selective
about how they use AI technology. Instead of going for
blanket automation, they should concentrate on technologies
that build on the region's assets, such its people and its
propensity to innovate in sustainability.
In the end, stakeholders should think about these actions:
businesses should do capacity assessments to find AI
opportunities that fit with their sector's RBV profile;
policymakers should make tiered governance rules that
protect ethics while also encouraging innovation, based on the
results of this study; and researchers should do
multidisciplinary studies that combine technical AI metrics
with organizational behavior and development economics.
This study eventually acts as a warning against technological
determinism, offering a paradigm for the adoption of human-
centered AI that reconciles academic rigor with the
socioeconomic realities of Latin America as Industry 5.0
transforms global competitiveness.
ACKNOWLEDGMENT
The author would like to thank all those involved in the
work who made it possible to achieve the objectives of the
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